FunChisq

Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) doi:10.48550/arXiv.1311.2707, an adapted functional chi-squared test (Kumar & Song 2022) doi:10.1093/bioinformatics/btac206, and an exact functional test (Zhong & Song 2019) doi:10.1109/TCBB.2018.2809743 (Nguyen et al. 2020) doi:10.24963/ijcai.2020/372. The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) doi:10.1038/nmeth.3773. A function index (Zhong & Song 2019) doi:10.1186/s12920-019-0565-9 (Kumar et al. 2018) doi:10.1109/BIBM.2018.8621502 derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.

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Last 365 days

This package has been downloaded 6,120 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Sep 11, 2024 with 81 downloads.

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